In this paper, we propose a novel approach for verification of on-line signatures based on user dependent feature selection and symbolic representation. Unlike other signature verification methods, which work with same features for all users, the proposed approach introduces the concept of user dependent features. It exploits the typicality of each and every user to select different features fo...

In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information. The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment. By using this methodology, a diagnostic system can be developed and its per...

This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based (CFS), best first search (BFS), dominance-based rough set approach (DRSA) methods. aims to enhance classifier’s performance in decision analysis by eliminating uncorrelated inconsistent values. The proposed method, na...

A top-down approach to word recognition is proposed. Discussions are presented on dynamically selecting the most effective feature combinations, which are applied to discriminate between a limited set of word hypotheses.

In this paper we describe feature selection experiments for on-line handwriting recognition. We performed a sequential forward search through a 25 elements set of on-line and pseudo-off-line features. In our experiments we obtained interesting results. Using a set of only five features, we achieved a performance that is similar that of the reference system that uses all features. The best subse...

Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introd...

Objectives: Genetic polymorphism interactions are among the important factors in affliction with complex diseases like Alzheimer’s disease. The important goal of genetic association studies is to identify a combination of polymorphisms and measure their importance in increasing the risk of occurrence of such diseases. In this study, feature selection approach of logic regression was used to ide...

Abstract Sample correlations and feature relations are two pieces of information that needed to be considered in the unsupervised selection, as labels missing guide model construction. Thus, we design a novel selection scheme, this paper, via considering completed sample dependencies unified framework. Specifically, self-representation graph construction conducted preserve select important neig...